Student-Performance-Classification-Analysis
This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por).
I have classified these students into three categories, "good", "fair", and "poor", according to their final exam performance. Then I analyzed a few features that have significant influence on students' final performance, including Romantic Status, Alcohol Consumption, Parents Education Level, Frequency Of Going Out, Desire Of Higher Education and Living Area. Finally, leveraging avaiable features, I have created various machine learning models to predict students' final performance classification and have compared models performance based on one-out sample accuracy score.
Table of Content
- Import Packages
- Load Dataset
- Data Preparation
- EDA
- 4.1 Final Grade Distribution
- 4.2 Correlation Heatmap
- 4.3 Romantic Status
- 4.4 Alcohol Consumption
- 4.5 Parents Education Level
- 4.6 Frequency Of Going Out
- 4.7 Desire Of Higher Education
- 4.8 Urban Vs. Rural Students
- Classification
- 5.1 Prepare Dataset For Modelling
- 5.2 Decision Tree Classifier
- 5.3 Random Forest Classifier
- 5.4 Support Vector Classifier
- 5.5 Logistic Regression Classifier
- 5.6 Ada Boost Classifier
- 5.7 Sochastic Gradient Descent Classifier
- 5.8 Model Selection
- Summary